Papers › Rethinking Complex Neural Network Architectures for Document Classification
Rethinking Complex Neural Network Architectures for Document Classification
Ashutosh Adhikari, Achyudh Ram, Raphael Tang, Jimmy Lin
Neural network models for many NLP tasks have grown increasingly complex in recent years, making training and deployment more difficult. A number of recent papers have questioned the necessity of such architectures and found that well-executed, simpler models are quite effective. We show that this is also the case for document classification: in a large-scale reproducibility study of several recent neural models, we find that a simple BiLSTM architecture with appropriate regularization yields accuracy and F1 that are either competitive or exceed the state of the art on four standard benchmark datasets. Surprisingly, our simple model is able to achieve these results without attention mechanisms. While these regularization techniques, borrowed from language modeling, are not novel, to our knowledge we are the first to apply them in this context. Our work provides an open-source platform and the foundation for future work in document classification.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Classification | IMDb-M | LSTM-reg (single model) | Accuracy | 52.8 | #2 of 2 | Archive leaderboard | report |
| Document Classification | Reuters-21578 | LSTM-reg (single model) | F1 | 87.0 | #7 of 8 | Archive leaderboard | report |
| Text Classification | Yelp-5 | LSTM-reg (single moedl) | Accuracy | 68.7% | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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